MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibration

TL;DR

MULi-Ev is a novel deep learning framework for real-time, online calibration of event cameras with LiDAR, improving sensor alignment for autonomous vehicle perception systems.

Contribution

It introduces the first online, deep learning-based method for calibrating event cameras with LiDAR, enabling dynamic adjustments during operation.

Findings

Achieves significant calibration accuracy improvements.

Demonstrates robustness in real-world scenarios.

Enhances sensor integration for autonomous driving.

Abstract

Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing comprehensive environmental information - remains unexplored. We introduce MULi-Ev, the first online, deep learning-based framework tailored for the extrinsic calibration of event cameras with LiDAR. This advancement is instrumental for the seamless integration of LiDAR and event cameras, enabling dynamic, real-time calibration adjustments that are essential for maintaining optimal sensor alignment amidst varying operational conditions. Rigorously evaluated against the real-world scenarios presented in the DSEC dataset, MULi-Ev not only achieves substantial improvements in calibration accuracy but also sets a new standard for integrating LiDAR with event cameras in mobile platforms. Our findings reveal the potential of MULi-Ev to bolster the safety, reliability, and overall performance of event-based perception systems in autonomous driving, marking a significant step forward in their real-world deployment and effectiveness.